{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pytorch_logistic_regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XjaQuuS2QVQ",
        "colab_type": "text"
      },
      "source": [
        "# Implementing A Logistic Regression Model from Scratch with PyTorch\n",
        "\n",
        "> \"Learn how to implement the fundamental building blocks of a neural network using PyTorch.\"\n",
        "\n",
        "- toc: false\n",
        "- branch: master\n",
        "- author: Elvis Saravia\n",
        "- badges: true\n",
        "- comments: true\n",
        "- categories: [machine learning, beginner, logistic regression]\n",
        "- image: images/logistic-regression.png\n",
        "- hide: false"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NXlGyUPQnZEk",
        "colab_type": "text"
      },
      "source": [
        "## About\n",
        "\n",
        "![alt text](https://drive.google.com/uc?export=view&id=11Bv3uhZtVgRVYVWDl9_ZAYQ0GU36LhM9)\n",
        "\n",
        "\n",
        "In this tutorial, we are going to implement a logistic regression model from scratch with PyTorch. The model will be designed with neural networks in mind and will be used for a simple image classification task. I believe this is a great approach to begin understanding the fundamental building blocks behind a neural network. Additionally, we will also look at best practices on how to use PyTorch for training neural networks.\n",
        "\n",
        "After completing this tutorial the learner is expected to know the basic building blocks of a logistic regression model. The learner is also expected to apply the logistic regression model to a binary image classification problem of their choice using PyTorch code.\n",
        "\n",
        "---\n",
        "\n",
        "**Author:** Elvis Saravia ( [Twitter](https://twitter.com/omarsar0) | [LinkedIn](https://www.linkedin.com/in/omarsar/))\n",
        "\n",
        "**Complete Code Walkthrough:** [Blog post](https://medium.com/dair-ai/implementing-a-logistic-regression-model-from-scratch-with-pytorch-24ea062cd856?source=friends_link&sk=49dcddb17d1d021d2d677f3666c88463)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gnyYNkr2Vub",
        "colab_type": "code",
        "outputId": "6aa7ba01-8d56-43ac-d4e1-f7e56f5c4335",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "## Import the usual libraries\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "from torchvision import datasets, models, transforms\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "## print out the pytorch version used (1.31 at the time of this tutorial)\n",
        "print(torch.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.3.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7Boavtx22CS",
        "colab_type": "code",
        "outputId": "891e7b3e-7a33-49b6-af28-b4d48f720f65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "## configuration to detect cuda or cpu\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print (device)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSpmMANNj5Uz",
        "colab_type": "text"
      },
      "source": [
        "## Importing Dataset\n",
        "In this tutorial we will be working on an image classification problem. You can find the public dataset [here](https://download.pytorch.org/tutorial/hymenoptera_data.zip). \n",
        "\n",
        "The objective of our model is to learn to classify between \"bee\" vs. \"no bee\" images.\n",
        "\n",
        "Since we are using Google Colab, we will need to first import our data into our environment using the code below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_A_mP5k627a6",
        "colab_type": "code",
        "outputId": "e46ccc9b-6bef-4c57-b854-d8e211c1b344",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "## importing dataset\n",
        "from google.colab import drive\n",
        "drive.mount('gdrive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "doUww-u_37dw",
        "colab_type": "text"
      },
      "source": [
        "## Data Transformation\n",
        "This is an image classification task, which means that we need to perform a few transformations on our dataset before we train our models. I used similar transformations as used in this [tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html#transfer-learning-for-computer-vision-tutorial). For a detailed overview of each transformation take a look at the official torchvision [documentation](https://pytorch.org/docs/stable/torchvision/transforms.html).\n",
        "\n",
        "The following code block performs the following operations:\n",
        "- The `data_transforms` contains a series of transformations that will be performed on each image found in the dataset. This includes cropping the image, resizing the image, converting it to tensor, reshaping it, and normalizing it. \n",
        "- Once those transformations have been defined, then the `DataLoader` function is used to automatically load the datasets and perform any additional configuration such as shuffling, batches, etc.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "501gdjiu6v24",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## configure root folder on your gdrive\n",
        "data_dir = 'gdrive/My Drive/DAIR RESOURCES/TF to PT/datasets/hymenoptera_data'\n",
        "\n",
        "## custom transformer to flatten the image tensors\n",
        "class ReshapeTransform:\n",
        "    def __init__(self, new_size):\n",
        "        self.new_size = new_size\n",
        "\n",
        "    def __call__(self, img):\n",
        "        result = torch.reshape(img, self.new_size)\n",
        "        return result\n",
        "\n",
        "## transformations used to standardize and normalize the datasets\n",
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.Resize(224),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        ReshapeTransform((-1,)) # flattens the data\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(224),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        ReshapeTransform((-1,)) # flattens the data\n",
        "    ]),\n",
        "}\n",
        "\n",
        "## load the correspoding folders\n",
        "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
        "                                          data_transforms[x])\n",
        "                  for x in ['train', 'val']}\n",
        "\n",
        "## load the entire dataset; we are not using minibatches here\n",
        "train_dataset = torch.utils.data.DataLoader(image_datasets['train'],\n",
        "                                            batch_size=len(image_datasets['train']),\n",
        "                                            shuffle=True)\n",
        "test_dataset = torch.utils.data.DataLoader(image_datasets['val'],\n",
        "                                           batch_size=len(image_datasets['val']),\n",
        "                                           shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OP9zr48w9xZ3",
        "colab_type": "text"
      },
      "source": [
        "## Print sample\n",
        "It's always a good practise to take a quick look at the dataset before training your models. Below we print out an example of one of the images from the `train_dataset`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BY3MOpT-5U4Q",
        "colab_type": "code",
        "outputId": "a7a1e58d-c733-48e3-9597-43d693591c31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "## load the entire dataset\n",
        "x, y = next(iter(train_dataset))\n",
        "\n",
        "## print one example\n",
        "dim = x.shape[1]\n",
        "print(\"Dimension of image:\", x.shape, \"\\n\", \n",
        "      \"Dimension of labels\", y.shape)\n",
        "\n",
        "plt.imshow(x[160].reshape(1, 3, 224, 224).squeeze().T.numpy())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimension of image: torch.Size([244, 150528]) \n",
            " Dimension of labels torch.Size([244])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fcab44f1c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pakNdOfJ4wDYchiAcSmhkgCEeAUjxwQKG4Cr6LiKOM5SKkFKjI00Qhr5pSJKMNg6cXi3Z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zMiCwSAKJjElbKQLOD3g/4EN8fN7xhVgEnIuDHXbsMENP27TRtW4cQ28ZxoC1nr7vaJsmUnSd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\nABUh6pApw6rNAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNKG-_uLZtQ7",
        "colab_type": "text"
      },
      "source": [
        "## Building the Model\n",
        "Let's now implement our [logistic regression](https://en.wikipedia.org/wiki/Logistic_regression) model. Logistic regression is one in a family of machine learning techniques that are used to train binary classifiers. They are also a great way to understand the fundamental building blocks of neural networks, thus they can also be considered the simplest of neural networks where the model performs a `forward` and `backward` propagation to train the model on the data provided. \n",
        "\n",
        "If you don't fully understand the structure of the code below, I strongly recommend you to read the following [tutorial](https://medium.com/dair-ai/pytorch-1-2-introduction-guide-f6fa9bb7597c), which I wrote for PyTorch beginners. You can also check out [Week 2](https://www.coursera.org/learn/neural-networks-deep-learning/home/week/2) of Andrew Ng's Deep Learning Specialization course for all the explanation, intuitions, and details of the different parts of the neural network such as the `forward`, `sigmoid`, `backward`, and `optimization` steps. \n",
        "\n",
        "In short:\n",
        "- The `__init__` function initializes all the parameters (`W`, `b`, `grad`) that will be used to train the model through backpropagation. \n",
        "- The goal is to learn the `W` and `b` that minimimizes the cost function which is computed as seen in the `loss` function below.\n",
        "\n",
        "Note that this is a very detailed implementation of a logistic regression model so I had to explicitly move a lot of the computations into the GPU for faster calcuation, `to(device)` takes care of this in PyTorch. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lH1_IRKwR8Zm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LR(nn.Module):\n",
        "    def __init__(self, dim, lr=torch.scalar_tensor(0.01)):\n",
        "        super(LR, self).__init__()\n",
        "        # intialize parameters\n",
        "        self.w = torch.zeros(dim, 1, dtype=torch.float).to(device)\n",
        "        self.b = torch.scalar_tensor(0).to(device)\n",
        "        self.grads = {\"dw\": torch.zeros(dim, 1, dtype=torch.float).to(device),\n",
        "                      \"db\": torch.scalar_tensor(0).to(device)}\n",
        "        self.lr = lr.to(device)\n",
        "\n",
        "    def forward(self, x):\n",
        "        ## compute forward\n",
        "        z = torch.mm(self.w.T, x)\n",
        "        a = self.sigmoid(z)\n",
        "        return a\n",
        "\n",
        "    def sigmoid(self, z):\n",
        "        return 1/(1 + torch.exp(-z))\n",
        "\n",
        "    def backward(self, x, yhat, y):\n",
        "        ## compute backward\n",
        "        self.grads[\"dw\"] = (1/x.shape[1]) * torch.mm(x, (yhat - y).T)\n",
        "        self.grads[\"db\"] = (1/x.shape[1]) * torch.sum(yhat - y)\n",
        "    \n",
        "    def optimize(self):\n",
        "        ## optimization step\n",
        "        self.w = self.w - self.lr * self.grads[\"dw\"]\n",
        "        self.b = self.b - self.lr * self.grads[\"db\"]\n",
        "\n",
        "## utility functions\n",
        "def loss(yhat, y):\n",
        "    m = y.size()[1]\n",
        "    return -(1/m)* torch.sum(y*torch.log(yhat) + (1 - y)* torch.log(1-yhat))\n",
        "\n",
        "def predict(yhat, y):\n",
        "    y_prediction = torch.zeros(1, y.size()[1])\n",
        "    for i in range(yhat.size()[1]):\n",
        "        if yhat[0, i] <= 0.5:\n",
        "            y_prediction[0, i] = 0\n",
        "        else:\n",
        "            y_prediction[0, i] = 1\n",
        "    return 100 - torch.mean(torch.abs(y_prediction - y)) * 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N8SZXITgS5sQ",
        "colab_type": "text"
      },
      "source": [
        "## Pretesting the Model\n",
        "It is also good practice to test your model and make sure the right steps are taking place before training the entire model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L40JX-aXS3cP",
        "colab_type": "code",
        "outputId": "5a12a9de-2ca6-4bb1-91e4-e4599b4c23b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "## model pretesting\n",
        "x, y = next(iter(train_dataset))\n",
        "\n",
        "## flatten/transform the data\n",
        "x_flatten = x.T\n",
        "y = y.unsqueeze(0) \n",
        "\n",
        "## num_px is the dimension of the images\n",
        "dim = x_flatten.shape[0]\n",
        "\n",
        "## model instance\n",
        "model = LR(dim)\n",
        "model.to(device)\n",
        "yhat = model.forward(x_flatten.to(device))\n",
        "yhat = yhat.data.cpu()\n",
        "\n",
        "## calculate loss\n",
        "cost = loss(yhat, y)\n",
        "prediction = predict(yhat, y)\n",
        "print(\"Cost: \", cost)\n",
        "print(\"Accuracy: \", prediction)\n",
        "\n",
        "## backpropagate\n",
        "model.backward(x_flatten.to(device), yhat.to(device), y.to(device))\n",
        "model.optimize()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cost:  tensor(0.6931)\n",
            "Accuracy:  tensor(50.4098)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJiwRC7ecBBw",
        "colab_type": "text"
      },
      "source": [
        "## Train the Model\n",
        "It's now time to train the model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4pS54kMTT0n",
        "colab_type": "code",
        "outputId": "f9b5c5ac-3365-4e0c-8ec2-4cdb34bbddae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "## hyperparams\n",
        "costs = []\n",
        "dim = x_flatten.shape[0]\n",
        "learning_rate = torch.scalar_tensor(0.0001).to(device)\n",
        "num_iterations = 100\n",
        "lrmodel = LR(dim, learning_rate)\n",
        "lrmodel.to(device)\n",
        "\n",
        "## transform the data\n",
        "def transform_data(x, y):\n",
        "    x_flatten = x.T\n",
        "    y = y.unsqueeze(0) \n",
        "    return x_flatten, y \n",
        "\n",
        "## training the model\n",
        "for i in range(num_iterations):\n",
        "    x, y = next(iter(train_dataset))\n",
        "    test_x, test_y = next(iter(test_dataset))\n",
        "    x, y = transform_data(x, y)\n",
        "    test_x, test_y = transform_data(test_x, test_y)\n",
        "\n",
        "    # forward\n",
        "    yhat = lrmodel.forward(x.to(device))\n",
        "    cost = loss(yhat.data.cpu(), y)\n",
        "    train_pred = predict(yhat, y)\n",
        "        \n",
        "    # backward\n",
        "    lrmodel.backward(x.to(device), \n",
        "                    yhat.to(device), \n",
        "                    y.to(device))\n",
        "    lrmodel.optimize()\n",
        "    ## test\n",
        "    yhat_test = lrmodel.forward(test_x.to(device))\n",
        "    test_pred = predict(yhat_test, test_y)\n",
        "\n",
        "    if i % 10 == 0:\n",
        "        costs.append(cost)\n",
        "\n",
        "    if i % 10 == 0:\n",
        "        print(\"Cost after iteration {}: {} | Train Acc: {} | Test Acc: {}\".format(i, \n",
        "                                                                                    cost, \n",
        "                                                                                    train_pred,\n",
        "                                                                                    test_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cost after iteration 0: 0.6931470036506653 | Train Acc: 50.40983581542969 | Test Acc: 45.75163269042969\n",
            "Cost after iteration 10: 0.6691471934318542 | Train Acc: 64.3442611694336 | Test Acc: 54.24836730957031\n",
            "Cost after iteration 20: 0.6513187885284424 | Train Acc: 68.44261932373047 | Test Acc: 54.24836730957031\n",
            "Cost after iteration 30: 0.6367831230163574 | Train Acc: 68.03278350830078 | Test Acc: 54.24836730957031\n",
            "Cost after iteration 40: 0.6245343685150146 | Train Acc: 69.67213439941406 | Test Acc: 54.90196228027344\n",
            "Cost after iteration 50: 0.6139233112335205 | Train Acc: 70.90164184570312 | Test Acc: 56.20914840698242\n",
            "Cost after iteration 60: 0.6045243740081787 | Train Acc: 72.54098510742188 | Test Acc: 56.86274337768555\n",
            "Cost after iteration 70: 0.5960519909858704 | Train Acc: 74.18032836914062 | Test Acc: 57.51633834838867\n",
            "Cost after iteration 80: 0.5883094668388367 | Train Acc: 73.77049255371094 | Test Acc: 57.51633834838867\n",
            "Cost after iteration 90: 0.581156849861145 | Train Acc: 74.59016418457031 | Test Acc: 58.1699333190918\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CN5v7F1h1uuz",
        "colab_type": "text"
      },
      "source": [
        "## Result\n",
        "From the loss curve below you can see that the model is sort of learning to classify the images given the decreas in the loss. I only ran the model for `100` iterations. Train the model for many more rounds and analyze the results. In fact, I have suggested a couple of experiments and exercises at the end of the tutorial that you can try to get a more improved model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sN-m0_a8mx8Z",
        "colab_type": "code",
        "outputId": "a5427e00-e77c-4f06-c2bf-191662e88679",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "## the trend in the context of loss\n",
        "plt.plot(costs)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3lYs6NtewzilQ0YtItbPzwBFeXprL1MVb2Lr3EE3rx3FlegpjzkkltYk+dftd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\nohcRCXMqehGRMKeiFxEJc/8fHoDum58r8XcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lsfjo1DQLQBJ",
        "colab_type": "text"
      },
      "source": [
        "## Exercises\n",
        "There are many improvements and different experiments that you can perform on top of this notebook to keep practising ML:\n",
        "- It is always good to normalize/standardize your images which helps with learning. As an experiment, you can research and try different ways to standarize the dataset. We have normalized the dataset with the builtin PyTorch [normalizer](https://pytorch.org/docs/stable/torchvision/transforms.html#torchvision.transforms.Normalize) which uses the mean and standard deviation. Alternatively, you can simply divide the original pixel values by `255` which is what a lot of ML engineers do. Play around with this idea, and try different transformation or normalization techniques. What effect does this have on learning in terms of `speed` and `loss`?\n",
        "- The dataset is too small so our model is not really learning effectively. You can try many things to help with learning such as playing around with the learning rate. Try to decrease and increase the learning rate and observe the effect of this in learning? \n",
        "- If you explored the dataset further, you may have noticed that all the \"no-bee\" images are actually \"ant\" images. If you would like to create a more robust model, you may want to make your \"no-bee\" images more random and diverse. Additionally, the dataset is also being shuffled which you can easily disable in the data transformation section. What happens if you disable the shuffling?\n",
        "- The model is not really performing that well because of the dataset I am using and because I didn't train it for long enough. It is a relatively small dataset but the performance should get better with more training over time. A more challenging task involves adopting the model to other datasets. Give it a try!\n",
        "- Another important part that is missing in this tutorial is the comprehensive analysis of the model results. If you understand the code, it should be easy to figure out how to test with a few examples. In fact, it would also be great if you can put aside a small testing dataset for this part of the exercise, so as to test the generalization capabilities of the model.\n",
        "- We built the logistic regression model from scratch but with libraries like PyTorch, these days you can simply leverage the high-level functions that implement certain parts of the neural network for you. This simplifies your code and minimizes the amount of bugs in your code. Plus you don't have to code your neural networks from scratch all the time. As a bonus exercise, try to adapt PyTorch builtin modules and functions for implementing a simpler version of the above logistic regression model. I will also add this as a to-do task for myself and post a solution soon. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CXs3Nx0BIQYZ",
        "colab_type": "text"
      },
      "source": [
        "## References\n",
        "- [Understanding the Impact of Learning Rate on Neural Network Performance](https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/)\n",
        "- [TRANSFER LEARNING FOR COMPUTER VISION TUTORIAL](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html#transfer-learning-for-computer-vision-tutorial)\n",
        "- [Deep Learning Specialization by Andrew Ng](https://www.coursera.org/learn/neural-networks-deep-learning/home/welcome)"
      ]
    }
  ]
}